By routinely measuring personality, researchers might be able to minimize missing study data.

A study by researchers at UC Davis has found that giving clinical trial participants a simple personality test can predict which ones are more likely to drop out of a study or miss data collection appointments and potentially "wreak havoc" with study outcomes.

"Missing data wreak havoc with results and the statistical methods that we have to deal with them are questionable at best," said Anthony Jerant, an associate professor of family and community medicine in the UC Davis School of Medicine and the study's lead author.

"The loss of data due to participants dropping out or missing data collection appointments may account for why some interventions that seem to work during clinical trials fail when made available to the general public as drugs or therapies," Jerant said. "The results of our study make the case for more routine measurement of personality during enrollment of participants in randomized clinical trials."

Five-Factor Model of personality

The study, which is published in the Annals of Family Medicine, is one of the few to apply a well-established model in psychology research — the Five-Factor Model of personality — to the analysis of data generated by medical studies in general and randomized clinical trials in particular.

Jerant and his colleagues conducted their analyses using data from a randomized clinical trial that examined the effectiveness of using peer health educators to improve self-management of chronic illnesses among patients aged 40 and older.

The Five-Factor Model has been widely used in psychology research because it allows scientists to measure the level (or amount) of five fundamental personality factors — neuroticism, extraversion, openness, agreeableness and conscientiousness — within each individual.

Improving the accuracy of study findings

Jerant said that by routinely measuring personality using this model, researchers might be able to minimize missing study data, and thus improve the accuracy of their findings by targeting participants deemed most likely to drop out and keeping them engaged in the study. The measures can also be used to further improve the accuracy of study findings by adjusting for personality during data analysis — just as researchers now routinely adjust for factors like age, gender and socioeconomic status.

"We already know that study participants are a self-selected group of people who are different from the general population," Jerant said. "Measuring personality using the Five-Factor Model helps us know just how different our study populations are and allows us to adjust for those differences, making study results more applicable to the general population."

In an examination of the Five-Factor Model, Jerant and his colleagues found that higher levels of three of the five factors — openness (being open to new ideas and experiences), agreeableness (wanting to please others) and conscientiousness (being achievement-driven) — helped predict which participants would drop out of the study early or fail to keep data-collection appointments.

"We found that personality was a powerful predictor of which participants would miss appointments and contribute to missing study data," Jerant said.

Five-Factor Model tests are not expensive or time-consuming to administer, he said. "We think that personality is important to account for and, if done routinely in medical research, might really improve our ability to predict the success of drug therapies and other health interventions when they eventually get applied to the general population," Jerant said. "We saw a pretty large effect of personality on missing data, above and beyond the effects of other variables now routinely measured in research studies."

Clinical trials at UC Davis

Breakthroughs that address the health challenges facing our world are at the core of UC Davis Health System's mission to discover and share knowledge to advance health.

UC Davis conducts more than 1,000 research studies annually, including basic science, translational and clinical trial research — all with the goal of bringing new, effective and safe treatments to patients more quickly.

Jerant said he hopes the study will encourage clinical trial researchers to include personality assessments in their study designs. He cautioned, however, that the current study results may not be equally helpful in all instances.

"Every study is different and different personality factors may be important among different groups," he said.

Jerant and his team have also recently completed analyses of data from a different clinical trial showing that personality helped predict which people aged 75 and older were less likely to take their assigned study medication.

Other study authors include Peter Franks of the UC Davis Department of Family and Community Medicine and Center for Healthcare Policy and Research, and Benjamin Chapman and Paul Duberstein of the Laboratory of Personality and Development at the University of Rochester Medical Center. The study was funded by grants from the National Institutes of Health and the Agency for Healthcare Research and Quality.

UC Davis School of Medicine is among the nation's leading medical schools, recognized for its specialty-and primary-care programs.The school offers combined medical and master's degree programs in public health, business administration, and rural health, as well as a combined medical and doctoral degree for physician scientists interested in addressing specific scientific, social, ethical and political challenges of health care. Along with being a leader in health-care research, the school is known for its commitment to people from underserved communities and a passion for clinical care. For more information, visit UC Davis School of Medicine.